In hierarchical cognitive radio networks, edge or cloud servers utilize the data collected by edge devices for modulation classification, which, however, is faced with problems of the computation load, transmission overhead, and data privacy. In this article, an edge learning (EL) based framework jointly mobilizing the edge device and the edge server for intelligent co-inference is proposed to realize the collaborative automatic modulation classification (C-AMC) between them. A spectrum semantic compression neural network is designed for the edge device to compress the collected raw data into a compact semantic embedding that is then sent to the edge server via the wireless channel. On the edge server side, a modulation classification neural network combining the bidirectional long-short term memory and attention structures is elaborated to determine the modulation type from the noisy semantic embedding. The C-AMC framework decently balances the computation resources of both sides while avoiding the high transmission overhead and data privacy leakage. Both the offline and online training procedures of the C-AMC framework are elaborated. The compression strategy of the C-AMC framework is also developed to further facilitate the deployment, especially for the resource-constrained edge device. Simulation results show the superiority of the EL-based C-AMC framework in terms of the classification accuracy, computational complexity, and the data compression rate as well as reveal useful insights paving the practical implementation.

中文翻译：

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基于边缘学习的分层认知无线电网络协作自动调制分类


在分层认知无线电网络中，边缘或云服务器利用边缘设备收集的数据进行调制分类，然而，这面临着计算负载、传输开销和数据隐私的问题。本文提出了一种基于边缘学习（EL）的框架，联合动员边缘设备和边缘服务器进行智能协同推理，以实现它们之间的协作自动调制分类（C-AMC）。为边缘设备设计了频谱语义压缩神经网络，将收集到的原始数据压缩为紧凑的语义嵌入，然后通过无线通道发送到边缘服务器。在边缘服务器端，精心设计了结合双向长短期记忆和注意力结构的调制分类神经网络，以根据噪声语义嵌入确定调制类型。 C-AMC框架很好地平衡了双方的计算资源，同时避免了高传输开销和数据隐私泄漏。详细阐述了C-AMC框架的离线和在线培训程序。 C-AMC框架的压缩策略也是为了进一步方便部署，特别是对于资源受限的边缘设备。仿真结果显示了基于 EL 的 C-AMC 框架在分类精度、计算复杂度和数据压缩率方面的优越性，并揭示了为实际实现铺平道路的有用见解。